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Commit 4edd5a2e authored by Alexander Gepperth's avatar Alexander Gepperth
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......@@ -195,25 +195,25 @@
\small
\SetAlgoLined
\caption{Adiabatic Replay}\label{alg:two}
\KwData{AR scholar $\Phi$, AR solver $\Theta$, real data $\mathcal{X}_{t}$, $Y_t$}
\KwData{AR scholar/gen. $\Phi$, AR solver $\Theta$, real data $\mathcal{X}^{t}$, $Y^t$}
\For{$t \in 2...T$}{ % from T2...TN
\For{$\mathcal{B}_{N} \sim \mathcal{X}_{t}$}{ % iterate over merged batches
\tcp{Query scholar $\Phi$.}
$\sigma_{\mathcal{B}_{N}} \gets Forward(\Phi, \mathcal{B}_{N})$\;
\tcp{Propagate batch $\mathcal B_N$ though $\Phi$.}
$\sigma_{\mathcal{B}_{N}} \gets \Phi(\mathcal{B}_{N})$\;
% forward call on DCGMM with batch xs, returns logits from top layer
\tcp{Sample from the GMM of $\Phi$.}
$\mathcal{B}_{G} \gets SampleOp(\Phi, \sigma_{\mathcal{B}_{N}})$ \;
\tcp{Query batch of variants from $\Phi$.}
$\mathcal{B}_{G} \gets VarGen(\Phi, \sigma_{\mathcal{B}_{N}})$ \;
% perform a sampling op. from the probability density described by the GMM we traverse the network layers in a backwards direction, returns a batch of samples based on the prototype responses from the forward call on xs
\tcp{Add generated samples to $\mathcal{D}_{G}$.}
$\mathcal{D}_{G} \gets UpdateData(\mathcal{B}_{G})$
\tcp{Add gen. samples to $\mathcal{X}_{G}^t$.}
$\mathcal{X}_G^t \gets UpdateData(\mathcal{B}_{G})$
}
\For{$\mathcal{B}_{M} \sim (\mathcal{D}_{R_t} \cup \mathcal{D}_{G_t})$}{
\tcp{Update selected prototype of $\Phi$.}
\For{$\mathcal{B}_{M} \sim (\mathcal{X}^{t} \cup \mathcal{X}_{G}^t)$}{
\tcp{Update $\Phi$ and $\Theta$}
% Update GMM
$\Phi \gets SGD(\mathcal{B}_{M})$\;
\tcp{Update AR solver.}
$\Phi \gets SGD(\mathcal{B}_{M})$\; $\Theta \gets SGD(\Phi(\mathcal B_M), Y_t)$\;
%\tcp{Update AR solver.}
% Classifier with merged batch
$\Theta \gets SGD(\Phi(\mathcal B_M), Y_t)$\;
%$\Theta \gets SGD(\Phi(\mathcal B_M), Y_t)$\;
}
}
%TODO \caption{do we need a caption here?}
......
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